OpenRaiser/Intern-Atlas huggingface dataset:
Network-Destabilizing Attacks
Venue: arXiv cs.NI 2012
Year: 2012

The Border Gateway Protocol (BGP) sets up routes between the smaller networks that make up the Internet. Despite its crucial role, BGP is notoriously vulnerable to serious problems, including (1) propagation of bogus routing information due to attacks or misconfigurations, and (2) network instabilities in the form of persistent routing oscillations. The conditions required to avoid BGP instabilities are quite delicate. How, then, can we explain the observed stability of today's Internet in the face of common configuration errors and attacks? This work explains this phenomenon by first noticing that almost every observed attack and misconfiguration to date shares a common characteristic: even when a router announces egregiously bogus information, it will continue to announce the same bogus information for the duration of its attack/misconfiguration. We call these the "fixed-route attacks", and show that, while even simple fixed-route attacks can destabilize a network, the commercial routing policies used in today's Internet prevent such attacks from creating instabilities.
BPEL-RF: A formal framework for BPEL orchestrations integrating distributed resources
Venue: arXiv cs.SE 2012
Year: 2012

Web service compositions are gaining attention to develop complex web systems by combination of existing services. Thus, there are many works that leverage the advantages of this approach. However, there are only few works that use web service compositions to manage distributed resources. In this paper, we then present a formal model that combines orchestrations written in BPEL with distributed resources, by using WSRF.
AMC Model for Denial of Sleep Attack Detection
Venue: arXiv cs.NI 2012
Year: 2012

Due to deployment in hostile environment, wireless sensor network is vulnerable to various attacks. Exhausted sensor nodes in sensor network become a challenging issue because it disrupts the normal connectivity of the network. Affected nodes give rise to denial of service that resists to get the objective of sensor network in real life. A mathematical model based on Absorbing Markov Chain (AMC)is proposed for Denial of Sleep attack detection in sensor network. In this mechanism, whether sensor network is affected by denial of sleep attack or not can be decided by considering expected death time of sensor network under normal scenario.
Local-Optimality Guarantees for Optimal Decoding Based on Paths
Venue: arXiv cs.IT 2012
Year: 2012

This paper presents a unified analysis framework that captures recent advances in the study of local-optimality characterizations for codes on graphs. These local-optimality characterizations are based on combinatorial structures embedded in the Tanner graph of the code. Local-optimality implies both unique maximum-likelihood (ML) optimality and unique linear-programming (LP) decoding optimality. Also, an iterative message-passing decoding algorithm is guaranteed to find the unique locally-optimal codeword, if one exists. We demonstrate this proof technique by considering a definition of local-optimality that is based on the simplest combinatorial structures in Tanner graphs, namely, paths of length $h$. We apply the technique of local-optimality to a family of Tanner codes. Inverse polynomial bounds in the code length are proved on the word error probability of LP-decoding for this family of Tanner codes.
Outlier detection from ETL Execution trace
Venue: arXiv cs.DB 2012
Year: 2012

Extract, Transform, Load (ETL) is an integral part of Data Warehousing (DW) implementation. The commercial tools that are used for this purpose captures lot of execution trace in form of various log files with plethora of information. However there has been hardly any initiative where any proactive analyses have been done on the ETL logs to improve their efficiency. In this paper we utilize outlier detection technique to find the processes varying most from the group in terms of execution trace. As our experiment was carried on actual production processes, any outlier we would consider as a signal rather than a noise. To identify the input parameters for the outlier detection algorithm we employ a survey among developer community with varied mix of experience and expertise. We use simple text parsing to extract these features from the logs, as shortlisted from the survey. Subsequently we applied outlier detection technique (Clustering based) on the logs. By this process we reduced our domain of detailed analysis from 500 logs to 44 logs (8 Percentage). Among the 5 outlier cluster, 2 of them are genuine concern, while the other 3 figure out because of the huge number of rows involved.
Consensus on Moving Neighborhood Model of Peterson Graph
Venue: arXiv cs.DC 2012
Year: 2012

In this paper, we study the consensus problem of multiple agents on a kind of famous graph, Peterson graph. It is an undirected graph with 10 vertices and 15 edges. Each agent randomly walks on this graph and communicates with each other if and only if they coincide on a node at the same time. We conduct numerical study on the consensus problem in this framework and show that global consensus can be achieved.
Classify Participants in Online Communities
Venue: arXiv cs.SI 2012
Year: 2012

As online communities become increasingly popular, researchers have tried to examine participating activities in online communities as well as how to sustain online communities. However, relatively few studies have tried to understand what kinds of participants constitute online communities. In this study, we try to contribute online community research by developing "common language" to classify different participants in online communities. Specifically, we argue that the previous way to classify participants is not sufficient and accurate, and we propose a continuum to classify participants based on participants' overall trend of posting activities. In order to further online community research, we also propose potential directions for future studies.
Graph partitioning advance clustering technique
Venue: arXiv cs.LG 2012
Year: 2012

Clustering is a common technique for statistical data analysis, Clustering is the process of grouping the data into classes or clusters so that objects within a cluster have high similarity in comparison to one another, but are very dissimilar to objects in other clusters. Dissimilarities are assessed based on the attribute values describing the objects. Often, distance measures are used. Clustering is an unsupervised learning technique, where interesting patterns and structures can be found directly from very large data sets with little or none of the background knowledge. This paper also considers the partitioning of m-dimensional lattice graphs using Fiedler's approach, which requires the determination of the eigenvector belonging to the second smallest Eigenvalue of the Laplacian with K-means partitioning algorithm.
A new supervised non-linear mapping
Venue: arXiv cs.IR 2012
Year: 2012

Supervised mapping methods project multi-dimensional labeled data onto a 2-dimensional space attempting to preserve both data similarities and topology of classes. Supervised mappings are expected to help the user to understand the underlying original class structure and to classify new data visually. Several methods have been designed to achieve supervised mapping, but many of them modify original distances prior to the mapping so that original data similarities are corrupted and even overlapping classes tend to be separated onto the map ignoring their original topology. We propose ClassiMap, an alternative method for supervised mapping. Mappings come with distortions which can be split between tears (close points mapped far apart) and false neighborhoods (points far apart mapped as neighbors). Some mapping methods favor the former while others favor the latter. ClassiMap switches between such mapping methods so that tears tend to appear between classes and false neighborhood within classes, better preserving classes' topology. We also propose two new objective criteria instead of the usual subjective visual inspection to perform fair comparisons of supervised mapping methods. ClassiMap appears to be the best supervised mapping method according to these criteria in our experiments on synthetic and real datasets.
Design of modular wireless sensor
Venue: arXiv cs.SE 2012
Year: 2012

The paper addresses combinatorial approach to design of modular wireless sensor as composition of the sensor element from its component alternatives and aggregation of the obtained solutions into a resultant aggregated solution. A hierarchical model is used for the wireless sensor element. The solving process consists of three stages: (i) multicriteria ranking of design alternatives for system components/parts, (ii) composing the selected design alternatives into composite solution(s) while taking into account ordinal quality of the design alternatives above and their compatibility (this stage is based on Hierarchical Morphological Multicriteria Design - HMMD), and (iii) aggregation of the obtained composite solutions into a resultant aggregated solution(s). A numerical example describes the problem structuring and solving processes for modular alarm wireless sensor element.
BSP vs MapReduce
Venue: arXiv cs.DC 2012
Year: 2012

The MapReduce framework has been generating a lot of interest in a wide range of areas. It has been widely adopted in industry and has been used to solve a number of non-trivial problems in academia. Putting MapReduce on strong theoretical foundations is crucial in understanding its capabilities. This work links MapReduce to the BSP model of computation, underlining the relevance of BSP to modern parallel algorithm design and defining a subclass of BSP algorithms that can be efficiently implemented in MapReduce.
A Hybrid Image Cryptosystem Based On OMFLIP Permutation Cipher
Venue: arXiv cs.MM 2012
Year: 2012

The protection of confidential image data from unauthorized access is an important area of research in network communication. This paper presents a high-level security encryption scheme for gray scale images. The gray level image is first decomposed into binary images using bit scale decomposition. Each binary image is then compressed by selecting a good scanning path that minimizes the total number of bits needed to encode the bit sequence along the scanning path using two dimensional run encoding. The compressed bit string is then scrambled iteratively using a pseudo-random number generator and finally encrypted using a bit level permutation OMFLIP. The performance is tested, illustrated and discussed.
Regret Bounds for Deterministic Gaussian Process Bandits
Venue: arXiv cs.LG 2012
Year: 2012

This paper analyses the problem of Gaussian process (GP) bandits with deterministic observations. The analysis uses a branch and bound algorithm that is related to the UCB algorithm of (Srinivas et al., 2010). For GPs with Gaussian observation noise, with variance strictly greater than zero, (Srinivas et al., 2010) proved that the regret vanishes at the approximate rate of $O(\frac{1}{\sqrt{t}})$, where t is the number of observations. To complement their result, we attack the deterministic case and attain a much faster exponential convergence rate. Under some regularity assumptions, we show that the regret decreases asymptotically according to $O(e^{-\frac{τt}{(\ln t)^{d/4}}})$ with high probability. Here, d is the dimension of the search space and $τ$ is a constant that depends on the behaviour of the objective function near its global maximum.
Fixed-Rank Representation for Unsupervised Visual Learning
Venue: arXiv cs.CV 2012
Year: 2012

Subspace clustering and feature extraction are two of the most commonly used unsupervised learning techniques in computer vision and pattern recognition. State-of-the-art techniques for subspace clustering make use of recent advances in sparsity and rank minimization. However, existing techniques are computationally expensive and may result in degenerate solutions that degrade clustering performance in the case of insufficient data sampling. To partially solve these problems, and inspired by existing work on matrix factorization, this paper proposes fixed-rank representation (FRR) as a unified framework for unsupervised visual learning. FRR is able to reveal the structure of multiple subspaces in closed-form when the data is noiseless. Furthermore, we prove that under some suitable conditions, even with insufficient observations, FRR can still reveal the true subspace memberships. To achieve robustness to outliers and noise, a sparse regularizer is introduced into the FRR framework. Beyond subspace clustering, FRR can be used for unsupervised feature extraction. As a non-trivial byproduct, a fast numerical solver is developed for FRR. Experimental results on both synthetic data and real applications validate our theoretical analysis and demonstrate the benefits of FRR for unsupervised visual learning.
Modified Quine-McCluskey Method
Venue: arXiv cs.OH 2012
Year: 2012

The digital gates are basic electronic component of any digital circuit. Digital circuit should be simplified in order to reduce its cost by reducing number of digital gates required to implement it. To achieve this, we use Boolean expression that helps in obtaining minimum number of terms and does not contain any redundant pair. Karnaugh map(K-map) and Quine-McCluskey(QM) methods are well known methods to simplify Boolean expression. K-map method becomes complex beyond five variable Boolean expression. Quine-McCluskey method is computer based technique for minimization of Boolean function and it is faster than K-map method. This paper proposes E-sum based optimization to Quine-McCluskey Method to increase its performance by reducing number of comparisons between mintermlist in determination of prime implicants. Modified Quine-McCluskey method(MQM) can be implemented to any number of variable.
Discovering Algorithms with Matrix Code
Venue: arXiv cs.PL 2012
Year: 2012

In first-year programming courses it is often difficult to show students how an algorithm can be discovered. In this paper we present a program format that supports the development from specification to code in small and obvious steps; that is, a discovery process. The format, called Matrix Code, can be interpreted as a proof according to the Floyd-Hoare program verification method. The process consists of expressing the specification of a function body as an initial code matrix and then growing the matrix by adding rows and columns until the completed matrix is translated in a routine fashion to compilable code. As worked example we develop a Java program that generates the table of the first N prime numbers.
Minimum Cost Multicast with Decentralized Sources
Venue: arXiv cs.IT 2012
Year: 2012

In this paper we study the multisource multicast problem where every sink in a given directed acyclic graph is a client and is interested in a common file. We consider the case where each node can have partial knowledge about the file as a side information. Assuming that nodes can communicate over the capacity constrained links of the graph, the goal is for each client to gain access to the file, while minimizing some linear cost function of number of bits transmitted in the network. We consider three types of side-information settings:(ii) side information in the form of linearly correlated packets; and (iii) the general setting where the side information at the nodes have an arbitrary (i.i.d.) correlation structure. In this work we 1) provide a polynomial time feasibility test, i.e., whether or not all the clients can recover the file, and 2) we provide a polynomial-time algorithm that finds the optimal rate allocation among the links of the graph, and then determines an explicit transmission scheme for cases (i) and (ii).
Elements of Cellular Blind Interference Alignment --- Aligned Frequency Reuse, Wireless Index Coding and Interference Diversity
Venue: arXiv cs.IT 2012
Year: 2012

We explore degrees of freedom (DoF) characterizations of partially connected wireless networks, especially cellular networks, with no channel state information at the transmitters. Specifically, we introduce three fundamental elements --- aligned frequency reuse, wireless index coding and interference diversity --- through a series of examples, focusing first on infinite regular arrays, then on finite clusters with arbitrary connectivity and message sets, and finally on heterogeneous settings with asymmetric multiple antenna configurations. Aligned frequency reuse refers to the optimality of orthogonal resource allocations in many cases, but according to unconventional reuse patterns that are guided by interference alignment principles. Wireless index coding highlights both the intimate connection between the index coding problem and cellular blind interference alignment, as well as the added complexity inherent to wireless settings. Interference diversity refers to the observation that in a wireless network each receiver experiences a different set of interferers, and depending on the actions of its own set of interferers, the interference-free signal space at each receiver fluctuates differently from other receivers, creating opportunities for robust applications of blind interference alignment principles.
Primary User Traffic Estimation for Dynamic Spectrum Access
Venue: arXiv cs.NI 2012
Year: 2012

Accurate estimation of licensed channel Primary User's (PU) temporal statistics is important for Dynamic Spectrum Access (DSA) systems. With accurate estimation of the mean duty cycle, u, and the mean off- and on-times of PUs, DSA systems can more efficiently assign PU resources to its subscribers, thus, increasing channel utilization. This paper presents a mathematical analysis of the accuracy of estimating u, as well as the PU mean off- and on-times, where the estimation accuracy is expressed as the mean squared estimation error. The analysis applies for the traffic model assuming exponentially distributed PU off- and on-times, which is a common model in traffic literature. The estimation accuracy is quantified as a function of the number of samples and observation window length, hence, this work provides guidelines on traffic parameters estimation for both energy-constrained and delay-constrained applications. For estimating u, we consider uniform, non-uniform, and weighted sample stream averaging, as well as maximum likelihood estimation. The estimation accuracy of the mean PU off- and on-times is studied when maximum likelihood estimation is employed. Furthermore, we develop algorithms for the blind estimation of the traffic parameters based on the derived theoretical estimation accuracy expressions. We show that the estimation error for all traffic parameters is lower bounded for a fixed observation window length due to the correlation between the traffic samples. Moreover, we prove that for estimating u, maximum likelihood estimation can yield the same estimation error as weighted sample averaging using only half the observation window length.
Blind 3D Model Watermarking Based on Multi-Resolution Representation and Fuzzy Logic
Venue: arXiv cs.MM 2012
Year: 2012

Insertion of a text message, audio data or/and an image into another image or 3D model is called as a watermarking process. Watermarking has variety of applications like: Copyright Protection, Owner Identification, Copy Protection and Data Hiding etc., depending upon the type of watermark insertion algorithm. Watermark remains in the content after applying various attacks without any distortions. The blind watermarking method used in the system is based on a wavelet transform, a fuzzy inference system and a multi-resolution representation (MRR) of the 3d model. The watermark scrambled by Arnold Transform is embedded in the wavelet coefficients at third resolution level of the MRR. Fuzzy logic approach used in the method makes it to approximate the best possible gain with an accurate scaling factor so that the watermark remains invisible. The fuzzy input variables are computed for each wavelet coefficient in the 3D model. The output of the fuzzy system is a single value which is a perceptual value for each corresponding wavelet coefficient. Thus, the fuzzy perceptual mask combines all these non-linear variables to build a simple, easy to use HVS model. Results shows that the system is robust against affine transformations, smoothing, cropping and noise attacks.
A Simple Flood Forecasting Scheme Using Wireless Sensor Networks
Venue: arXiv cs.LG 2012
Year: 2012

This paper presents a forecasting model designed using WSNs (Wireless Sensor Networks) to predict flood in rivers using simple and fast calculations to provide real-time results and save the lives of people who may be affected by the flood. Our prediction model uses multiple variable robust linear regression which is easy to understand and simple and cost effective in implementation, is speed efficient, but has low resource utilization and yet provides real time predictions with reliable accuracy, thus having features which are desirable in any real world algorithm. Our prediction model is independent of the number of parameters, i.e. any number of parameters may be added or removed based on the on-site requirements. When the water level rises, we represent it using a polynomial whose nature is used to determine if the water level may exceed the flood line in the near future. We compare our work with a contemporary algorithm to demonstrate our improvements over it. Then we present our simulation results for the predicted water level compared to the actual water level.
Octopus: A Secure and Anonymous DHT Lookup
Venue: arXiv cs.CR 2012
Year: 2012

Distributed Hash Table (DHT) lookup is a core technique in structured peer-to-peer (P2P) networks. Its decentralized nature introduces security and privacy vulnerabilities for applications built on top of them; we thus set out to design a lookup mechanism achieving both security and anonymity, heretofore an open problem. We present Octopus, a novel DHT lookup which provides strong guarantees for both security and anonymity. Octopus uses attacker identification mechanisms to discover and remove malicious nodes, severely limiting an adversary's ability to carry out active attacks, and splits lookup queries over separate anonymous paths and introduces dummy queries to achieve high levels of anonymity. We analyze the security of Octopus by developing an event-based simulator to show that the attacker discovery mechanisms can rapidly identify malicious nodes with low error rate. We calculate the anonymity of Octopus using probabilistic modeling and show that Octopus can achieve near-optimal anonymity. We evaluate Octopus's efficiency on Planetlab with 207 nodes and show that Octopus has reasonable lookup latency and manageable communication overhead.
Analysis of Sparse MIMO Radar
Venue: arXiv cs.IT 2012
Year: 2012

We consider a multiple-input-multiple-output radar system and derive a theoretical framework for the recoverability of targets in the azimuth-range domain and the azimuth-range-Doppler domain via sparse approximation algorithms. Using tools developed in the area of compressive sensing, we prove bounds on the number of detectable targets and the achievable resolution in the presence of additive noise. Our theoretical findings are validated by numerical simulations.